Modern driver assistance systems are being increasingly equipped with an electronic traffic sign recognition system in order to, e.g., warn the driver in the event of speeding. For this purpose, a camera records images of the region in front of the vehicle and delivers corresponding image data to an onboard computer that analyzes and classifies the image data by means of an algorithm in order to identify a traffic sign therefrom.
Such a method is known from, e.g., DE 198 52 631 A1.
The aim of such methods for traffic sign recognition consists in minimizing the rejection rate, i.e., the share of signs that are not recognized or recognized wrongly, wherein it would be advantageous if all traffic signs were standardized with respect to their design, whereby the great variety of different traffic signs, particularly in road traffic in foreign countries, would be reduced. Therefore, several European countries partially agreed on a standardization of traffic signs (Vienna Convention on Road Signs and Signals), e.g., on a characteristic design of speed limit signs (circular sign having a red outer ring and a number indicating the speed limit).
In a detection phase of such methods for traffic sign recognition, image regions that may contain potential traffic signs are identified in the camera image. After that, in a second procedure step, these sign hypotheses are submitted to a classificator that decides whether a traffic sign is present in the image region and which traffic sign it is.
The schematic block diagram in FIG. 1 shows a computer-based traffic sign recognition system that operates according to such a method. According to this, a camera 1 records images of the surroundings and the corresponding image data are stored in a storage unit 3 of an information processing unit 2 that additionally comprises a detection unit 4, a classification unit 5, an output storage 6, and an output unit 7. In the detection unit 4, those image regions (sign hypotheses) are identified which are subsequently submitted to the classification unit 5. The recognized traffic signs are stored in the output storage 6 and are then available to the output unit 7 (e.g., to a central display or to a display of an instrument cluster) for indication to the driver.
The classificator or classification unit 5 may operate in a learning-based manner (known from, e.g., DE 10 2005 062 154 A1), i.e., it is appropriately trained in advance using a set of learning examples whose specific designs depend on the selected detection method. For example, a known method for speed limit recognition consists in searching for circles in the camera image by means of image processing phases during the detection phase and submitting the surrounding rectangle as an image detail to the classificator, wherein this feature “circles” defines a particular class of traffic signs.
A method for the recognition of circular objects in image data of an image sensor is known from, e.g., DE 10 2005 017 541 A1.
As explained above, most countries use speed limit signs that are standardized according to the Vienna Convention and are characterized in that only a centered numerical block indicating the speed limit is embedded in the traffic sign (see FIG. 2). This is the usual design of a speed limit sign.
In addition to these standardized speed limit signs, however, there are signs, e.g., in Austria or Belgium, whose designs differ from the standardized one. In these different signs, a smaller text (e.g., “km”) is supplemented to the relevant numerical block (see FIG. 3) or the relevant numerical block is not centered (see FIG. 4), which often results in the size of the numerical block differing from the standardized design shown in FIG. 2, too. In most cases, it is smaller than the standard size. Sometimes, a traffic sign according to FIG. 4 is also created by sticking something over or painting over a text supplement existing on the traffic sign.
Concerning a classificator that operates in a learning-based manner, these deviations from the usual design of a speed limit sign (centered numerical block on the traffic sign) will result in highly increased variability since learning or training examples must be generated and provided also for these variations. Furthermore, more sign hypotheses will pass through such a classificator on account of said increased variability so that there will be the risk of an increased false alarm rate (false positives).